A Generic Approach for Wheat Disease Classification and Verification Using Expert Opinion for Knowledge-Based Decisions

Haider, Waleej; Rehman, Aqeel-ur; Durrani, Muhammad; Rehman, Sadiq Ur

doi:10.1109/access.2021.3058582

Cited by 40 publications

(10 citation statements)

References 43 publications

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“…Then, the disease pair ð X 1 , X 2 Þ and the reference pair ðX 3 , X 1 Þ, respectively, go through BERT [16] to obtain the correlation representation [12] and [17] of the two elements in the word pair. Finally, concatenate [12] and [18], and use the feed forward neural network (FNN) to predict the upper and lower relationship.…”

Section: Icd10 Fusion Phrasesmentioning

confidence: 99%

“…Then, the input embedding is sent to the bidirectional 4 Disease Markers transformer model, and the output [CLS] contains the information about whether the two disease words are related. The final output [18] of the labeled [CLS] is used as the correlation representation in the classification task vector. Similarly, the reference pair X 3 , X 1 is sent to BERT to get [19].…”

Section: Icd10 Fusion Phrasesmentioning

confidence: 99%

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BERT-Based Clinical Name Entity Reorganization Model for Health Diagnosis

et al. 2022

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The National Health and Family Planning Commission requires medical institutions to use the International Classification of Diseases (ICD) codes. However, due to many commonly used words in clinical disease descriptions, the direct mapping matching rate between the diagnosis names entered in the electronic medical records and the ICD codes is low. In this paper, based on the actual diagnostic data on the regional health platform, a disease term map incorporating standard terms was constructed. Specifically, based on the rule algorithm based on the components of the disease, a data-enhanced BERT (bidirectional encoder representation from transformers) upper and lower relationship recognition algorithm is proposed. Synonymous upper and lower relationships identify diseases, and the hierarchical structure is further integrated. In addition, a task assignment based on the association map of disease departments is also proposed. Methods were used for manual verification, and finally, 94,478 disease entities formed a large-scale disease term map, including 1,460 synonymous relationships and 46,508 hyponymous relationships. Evaluation experiments show that, based on the disease term map and clinical diagnosis, the coverage rate of diagnostic data is 75.31% higher than direct mapping coding based on ICD. In addition, using the disease term map to code diseases automatically will shorten the coding time by about 59.75% compared with manual coding by doctors, and the correct rate is 85%.

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Section: Icd10 Fusion Phrasesmentioning

confidence: 99%

Section: Icd10 Fusion Phrasesmentioning

confidence: 99%

BERT-Based Clinical Name Entity Reorganization Model for Health Diagnosis

et al. 2022

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“…Each leaf node corresponds to the value of the object represented by the path from the root to the leaf node. Decision tree is a frequently used technique in data mining, which can be used to analyze data and make predictions [ 28 – 31 ]. The processes of constructing the heavy metal hazard decision trees are illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

Assessment of potential human health risks in aquatic products based on the heavy metal hazard decision tree

Lin

Ling

2021

BMC Bioinformatics

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Background Naturally existing and human-produced heavy metals are released into the environment and cannot be completely decomposed by microorganisms, but they continue to accumulate in water and sediments, causing organisms to be exposed to heavy metals. Results This study designs and proposes heavy metal hazard decision trees for aquatic products, which are divided into seven categories including pelagic fishes, inshore fishes, other fishes, crustaceans, shellfish, cephalopods, and algae. Based on these classifications, representative fresh and processed seafood products are at the root of the heavy metal hazard decision trees. This study uses 2,107 cases of eating 556 cooked fresh or processed seafood product samples. The constructions of the proposed decision trees consist of 12 heavy metals, which include inorganic arsenic (iAs), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), strontium (Sr), thallium (Tl), and zinc (Zn). The heavy metal concentrations in cooked fresh and processed seafood product samples are subjected to a food safety risk assessment. Conclusions The results indicate the relationships among the seven categories of aquatic products, the relationships among 12 heavy metals in aquatic products, and the relationships among potential human health risks. Finally, the proposed heavy metal hazard decision trees for aquatic products can be used as a reference model for researchers and engineers.

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“…Both the Plant Village dataset, which contains 55,448 images, and the expanded dataset, which has 61,486 images, were used to train the model. In the studies proposed in [8,9,10,11,12,13 [14,15,16,17,18] categorized numerous leaf diseases (SSD). Images of diseases seen on the leaves of commercially produced banana, sugarcane, cotton, potato, brinjal, carrot, Chile, rice, and wheat are included in this collection.…”

Section: Literature Surveymentioning

confidence: 99%

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

Malathi¹,

Sheela²

2023

Preprint

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Crop disease detection models require integration of multimodal tasks including contextual segmentation, feature representation & selection, classification & post-processing operations. A wide variety of machine learning & deep learning techniques are proposed by researchers to design such models, but most of them are either highly complex, or have limited efficiency when applied to real-time farm fields. To overcome these issues, this text proposes design of an efficient & novel ensemble engine for crop disease detection via bioinspired multidomain feature analysis. The proposed model uses Fuzzy Saliency Maps for identification of contextual disease regions, which are represented into multidomain feature sets. These sets use Fourier, Gabor, Convolutional, Wavelet and Cosine operations, which assist in extraction of multiple high-density features. The extracted features are reduced via a Bacterial Foraging Optimization (BFO) Model, which retains only higher variance feature sets. The retained features are classified by an ensemble model, that combines Naïve Bayes (NB), Multilayer Perceptron (MLP), Logistic Regression (LR), k Nearest Neighbors (kNN) and Support Vector Machine (SVM) classifiers. These classifiers are combined using a boosting model, that assists in retaining unique-correct class training information sets. Due to which the proposed model is able to improve the classification accuracy for Wheat, Tomato, Rice & Corn diseases by 9.4%, while improving precision by 3.5%, recall by 5.9% and reducing delay by 4.3% when compared with existing classification models under similar disease types.

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A Generic Approach for Wheat Disease Classification and Verification Using Expert Opinion for Knowledge-Based Decisions

Cited by 40 publications

References 43 publications

BERT-Based Clinical Name Entity Reorganization Model for Health Diagnosis

BERT-Based Clinical Name Entity Reorganization Model for Health Diagnosis

Assessment of potential human health risks in aquatic products based on the heavy metal hazard decision tree

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

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